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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:23478
- loss:ContrastiveLoss
base_model: denaya/indoSBERT-large
widget:
- source_sentence: 'Pekerja anak Indonesia: Buku panduan 2022 (pr & pasca pandemi)'
sentences:
- Statistik Perusahaan Hak Pengusahaan Hutan 2010
- Statistik Kriminal 2016
- ' Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
Negara, November 2020'
- source_sentence: Jumlah pascr tradisional, pusat perbelanjaan, dan toko modern tahun
2019
sentences:
- Profil Pasar Tradisional, Pusat Perbelanjaan, dan Toko Modern 2019
- Laporan Perekonomian Indonesia 2008
- Profil Industri Mikro dan Kecil 2006
- source_sentence: Survei biay ahidup (SBH) di Ternate tahun 2012
sentences:
- Laporan Bulanan Data Sosial Ekonomi Januari 2016
- Keadaan Angkatan kerja di Indonesia Agustus 2009
- Statistik Perdagangan Luar Negeri Indonesia Impor 2023 Buku I
- source_sentence: Direktori perwsahaan air minum, listrik, dan gas di kota tahun
2009
sentences:
- Statistik Indonesia 1991
- Direktori Perusahaan Air Minum Listrik dan Gas Kota 2009
- Direktori Eksportir Indonesia 2015
- source_sentence: Studi efisiensi industri manufaktr
sentences:
- Statistik Indonesia 2019
- Statistik Potensi Desa Provinsi Maluku 2011
- Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4
datasets:
- yahyaabd/bps-publication-pos-neg-pairs
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on denaya/indoSBERT-large
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstats semantic base v1 eval
type: allstats-semantic-base-v1-eval
metrics:
- type: pearson_cosine
value: 0.9658815836712943
name: Pearson Cosine
- type: spearman_cosine
value: 0.7841756166101173
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: allstat semantic base v1 test
type: allstat-semantic-base-v1-test
metrics:
- type: pearson_cosine
value: 0.9592021090962591
name: Pearson Cosine
- type: spearman_cosine
value: 0.7818288777895762
name: Spearman Cosine
---
# SentenceTransformer based on denaya/indoSBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) on the [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) dataset. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 256 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("yahyaabd/allstats-semantic-base-v1-3")
# Run inference
sentences = [
'Studi efisiensi industri manufaktr',
'Statistik Potensi Desa Provinsi Maluku 2011',
'Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test |
|:--------------------|:-------------------------------|:------------------------------|
| pearson_cosine | 0.9659 | 0.9592 |
| **spearman_cosine** | **0.7842** | **0.7818** |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
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## Training Details
### Training Dataset
#### bps-publication-pos-neg-pairs
* Dataset: [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) at [46a5cb7](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs/tree/46a5cb7b0d6b00e9ef6bb1bf0ab6b6628ab66a9b)
* Size: 23,478 training samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.84 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.77 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>0: ~72.40%</li><li>1: ~27.60%</li></ul> |
* Samples:
| query | doc | label |
|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
| <code>Direktori perusahaan perantara keuangan bukan koperasi tahun 2006 (SE)</code> | <code>Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2018-2022, Buku 2 Pulau Jawa-Bali</code> | <code>0</code> |
| <code>Informasi lengkap tentang PPLS 2011</code> | <code>Indeks Harga Perdagangan Besar Indonesia tahun 2005</code> | <code>0</code> |
| <code>Data konversi GKG ke beras tahun 2012</code> | <code>Indikator Ekonomi Juli 2023</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Evaluation Dataset
#### bps-publication-pos-neg-pairs
* Dataset: [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) at [46a5cb7](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs/tree/46a5cb7b0d6b00e9ef6bb1bf0ab6b6628ab66a9b)
* Size: 5,031 evaluation samples
* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.97 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.76 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~72.70%</li><li>1: ~27.30%</li></ul> |
* Samples:
| query | doc | label |
|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------|
| <code>Informasi angka tanaman berkhasiat ogbat dan tanaman hias di tahun 2005</code> | <code>Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2010-2013 - Buku 2 Pulau Jawa-Bali</code> | <code>0</code> |
| <code>Informasi lengkap statistik horsikultura tahun 2020</code> | <code>NERACA ENERGI INDONESIA 2017-2021</code> | <code>0</code> |
| <code>Statistik air bersih Indonesia periode 2014-2019</code> | <code>Profil Usaha Konstruksi Perorangan Provinsi Kalimantan Utara, 2022</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 8
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `eval_on_start`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine |
|:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:|
| 0 | 0 | - | 0.0053 | 0.7770 | - |
| 0.5450 | 200 | 0.0023 | 0.0005 | 0.7842 | - |
| 1.0899 | 400 | 0.0005 | 0.0002 | 0.7842 | - |
| 1.6349 | 600 | 0.0002 | 0.0002 | 0.7842 | - |
| 2.1798 | 800 | 0.0001 | 0.0001 | 0.7842 | - |
| 2.7248 | 1000 | 0.0001 | 0.0001 | 0.7842 | - |
| 3.2698 | 1200 | 0.0 | 0.0001 | 0.7842 | - |
| 3.8147 | 1400 | 0.0 | 0.0001 | 0.7842 | - |
| 4.3597 | 1600 | 0.0 | 0.0001 | 0.7842 | - |
| **4.9046** | **1800** | **0.0** | **0.0001** | **0.7842** | **-** |
| 5.4496 | 2000 | 0.0 | 0.0001 | 0.7842 | - |
| 5.9946 | 2200 | 0.0 | 0.0001 | 0.7842 | - |
| 6.5395 | 2400 | 0.0 | 0.0001 | 0.7842 | - |
| 7.0845 | 2600 | 0.0 | 0.0001 | 0.7842 | - |
| 7.6294 | 2800 | 0.0 | 0.0001 | 0.7842 | - |
| -1 | -1 | - | - | - | 0.7818 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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